Dynamic progress recognition and recommendations based on machine learning

ABSTRACT

Systems for dynamically recognizing progress and generating recommendations are provided. In some examples, a system may image data from an augmented reality device. The image data may include video images, still images, images of machine-readable code, and the like. The received image data may be analyzed in real-time to identify an object within the data. In some examples, machine learning may be used to identify one or more characteristics of the object. The identified characteristics may be compared to one or more pre-defined goals or limits and a notification may be generated based on the comparison. The notification may be transmitted to the augmented reality device and displayed on the augmented reality device. In some examples, based on the comparison, machine learning may be used to generate one or more recommendations and a notification may be generated including the recommendations and may be transmitted to the augmented reality device for display.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S.application Ser. No. 15/830,175 filed Dec. 4, 2017, and entitled“Dynamic Progress Recognition and Recommendations Based on MachineLearning,” which is incorporated herein by reference in its entirety.

BACKGROUND

Aspects of the disclosure relate to electrical computers, systems, andmachine learning. In particular, one or more aspects of the disclosurerelate to using machine learning to determine progress toward apre-defined goal or limit and/or generate recommendations.

Augmented reality and augmented reality devices are becoming more commonin everyday life. Augmented reality is often used to provide real-timeinformation that would otherwise be inaccessible or difficult for a userto access. In addition, augmented reality is often used to make variousprocess more efficient and reduce computing resources required toperform various functions. Accordingly, the use of augmented reality andaugmented reality devices to track progress toward a pre-defined limitand to display recommendations maybe advantageous.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with tracking progress toward a pre-defined limit inreal-time and generating and providing recommendations in real-time.

In some examples, a system, computing platform, or the like, may receivedata, such as image data, from, for example, an augmented reality deviceof a user. The image data may include video images of one or moreobjects, still images of one or more objects, images of machine-readablecode, and the like. In some examples, the received image data may beanalyzed in real-time to identify an object within the data. In somearrangements, object recognition, optical character recognition, and thelike, may be used to identify the object.

In some examples, machine learning may be used to identify or determineone or more characteristics of the object. The identified or determinedcharacteristics may be compared to one or more pre-defined goals orlimits. In some examples, a notification may be generated based on thecomparison. The notification may be transmitted to the augmented realitydevice and displayed on the augmented reality device.

In some examples, based on the comparison, machine learning may be usedto generate one or more recommendations or recommended alternatives. Insome examples, a notification may be generated including therecommendations or recommended alternatives and may be transmitted tothe augmented reality device for display.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A and 1B depict an illustrative computing environment forimplementing dynamic progress recognition and recommendation functionsin accordance with one or more aspects described herein;

FIGS. 2A-2C depict an illustrative event sequence for implementingdynamic progress recognition and recommendation functions in accordancewith one or more aspects described herein;

FIGS. 3A-3C illustrate one example system for implementing dynamicprogress recognition and recommendation functions in accordance with oneor more aspects described herein;

FIGS. 4A-4C illustrate another example system for implementing dynamicprogress recognition and recommendation functions in accordance with oneor more aspects described herein;

FIG. 5 depicts an illustrative method for implementing and using asystem to perform dynamic progress recognition and recommendationfunctions, according to one or more aspects described herein;

FIG. 6 illustrates one example operating environment in which variousaspects of the disclosure may be implemented in accordance with one ormore aspects described herein; and

FIG. 7 depicts an illustrative block diagram of workstations and serversthat may be used to implement the processes and functions of certainaspects of the present disclosure in accordance with one or more aspectsdescribed herein.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

Some aspects of the disclosure relate to using machine learning to trackprogress toward a pre-defined goal or limit, generate recommendations,and the like. The functions described herein may be performed inreal-time or near real-time to enable or facilitate informeddecisioning.

As mentioned above, augmented reality and augmented reality devices arebeing used to make various process more efficient, enable more informeddecisioning, and the like. Accordingly, aspects described herein providefor dynamic progress recognition and recommendations using machinelearning and performed using one or more augmented reality devices. Insome examples, image data may be captured via an augmented realitydevice, such as wearable, augmented reality glasses. Various otherdevices may also be used without departing from the invention. The imagedata may include an image of one or more objects. In some examples, theimage data may be analyzed to identify the one or more objects withinthe image data.

In some examples, machine learning may be used to identify one or morecharacteristics of the identified one or more objects. Thecharacteristics may then be compared to pre-defined goals or limits,such as pre-defined spending limit for or associated with a particularcategory of goods or services, and a notification may be generated basedon the comparison. The notification may include identification of aremaining amount toward the pre-defined goal or limit, an indication ofwhether purchasing the identified object would then exceed thepre-defined goal or limit, and the like.

In some examples, machine learning may be used to identify one or morerecommendations or recommended alternatives (e.g., recommendedalternative object) based on the comparison. The recommendedalternatives or recommendations may be included in a notification. Insome examples, the notification may be transmitted to the augmentedreality device and displayed on the augmented reality device. Forinstance, the notification may be displayed on, for example, a lens ofthe augmented reality device, while, for example the user issimultaneously viewing the object through the lens.

These and various other arrangements will be discussed more fully below.

FIGS. 1A and 1B depict an illustrative computing environment forimplementing and using a system for dynamically recognizing progress andproviding recommendations in accordance with one or more aspectsdescribed herein. Referring to FIG. 1A, computing environment 100 mayinclude one or more computing devices and/or other computing systems.For example, computing environment 100 may include a dynamic progressrecognition and recommendation computing platform 110, an internal datacomputing device 120, an external data computing device 130, anaugmented reality device 140, a first local user computing device 150, asecond local user computing device 155, a first remote user computingdevice 170, and a second remote user computing device 175.

Dynamic progress recognition and recommendation computing platform 110may be configured to host and/or execute a machine learning engine toprovide dynamic progress recognition and recommendation functions. Insome examples, data may be received from, for example, augmented realitydevice 140. The data may be image data (e.g., video, still images, orthe like), may include a machine scannable code, such as a bar code,quick response code, or the like. In some examples, the data may beanalyzed, e.g., in real-time or near real-time, to identify an objectwithin the received data. For instance, image recognition software,optical character recognition software, machine readable code scanningsoftware, or the like, may be used to identify one or more objectswithin the data. The dynamic progress recognition and recommendationcomputing platform 110 may then identify one or more characteristics ofthe object (e.g., price, nutritional value, or the like) and may comparethe object and characteristics to one or more pre-determined orpre-defined goals, limitations, or the like, of the user. For instance,the cost of the identified object may be compared to a pre-definedbudget for the user to determine whether purchasing the object wouldleave the user over budget.

In some examples, the dynamic progress recognition and recommendationcomputing platform 110 may generate a notification. In some examples,the notification may include an alternative option (e.g., a recommendedalternative object) for the user. In some arrangements, the notificationmay then be transmitted to the augmented reality device 140 and may bedisplayed on the device 140.

In some examples, validation data may be received by the dynamicprogress recognition and recommendation computing platform 110. Forinstance, the augmented reality device 140 may transmit an image of theuser purchasing the object. In another example, data may be receivedfrom a system (e.g., internal data computing device 120, external datacomputing device 130, local user computing device 150, local usercomputing device 155, remote user computing device 170, remote usercomputing device 175, or the like) indicating that the user purchasedthe object, purchased the recommended alternate object, or the like.This information may then be used to update and/or validate one or moremachine learning datasets.

Augmented reality device 140 may include one of various types ofaugmented reality devices without departing from the invention. Forinstance, the augmented reality device 140 may be or include a wearabledevice, such as augmented reality glasses. In these examples, the usermay view an object through one or more clear or semi-clear lenses withinthe augmented reality device 140. In some examples, the augmentedreality device 140 may include one or more image capture devices, suchas a camera or the like. In some arrangements, the camera may capture(e.g., via a lens of the camera different from the lenses through whichthe user is viewing the object) object data such as images, video, orthe like, while (e.g., simultaneously with) the user viewing the objectthrough the clear or semi-clear lenses arranged in the augmented realitydevice 140.

In some arrangements, one or more notifications may be displayed on theclear or semi-clear lenses of the augmented reality device 140. Forinstance, text, images, or other data may be displayed on the lens(es)and, in some examples, may overlay or be displayed simultaneously withthe viewing of the object through the clear or semi-clear lenses.

In some examples, the augmented reality device 140 may include awearable fitness tracker, smartphone, tablet computing device, or thelike, of the user.

Internal data computing device 120 may be configured to store, transmit,and/or receive data associated with one or more internal data orcomputer systems. For instance, an entity implementing the dynamicprogress recognition and recommendation computing platform 110 may storedata associated with various users, event processing device parameterinformation, account information, historical transaction or other userdata, user behavioral information associated with a device, and thelike. In some examples, this data may include purchase data that may beused to update and/or validate one or more machine learning datasets.This information may be transmitted, via the internal data computingdevice 120, to the dynamic progress recognition and recommendationcomputing platform 110 and may be used to generate or update one or moremachine learning datasets, generate one or more recommendedalternatives, generate a notification, and the like.

External data computing device 130 may be configured to store, transmit,and/or receive data associated with one or more data or computer systemsexternal to the entity implementing the dynamic progress recognition andrecommendation computing platform 110. For instance, data, such aspublicly available data, transaction data, user demographic data, socialmedia data, vehicle sensor data, and the like, may be transmitted, viathe external data computing device 130, from one or more data orcomputer systems, to the dynamic progress recognition and recommendationcomputing platform 110 and may be used to generate or update one or moremachine learning datasets, generate one or more recommendedalternatives, generate a notification, and the like.

Local user computing device 150, 155 and remote user computing device170, 175 may be configured to communicate with and/or connect to one ormore computing devices or systems shown in FIG. 1A. For instance, localuser computing device 150, 155 may communicate with one or morecomputing systems or devices via network 190, while remote usercomputing device 170, 175 may communicate with one or more computingsystems or devices via network 195. In some examples, local usercomputing device 150, 155 may be used to control or implement aspects ofthe functions performed by dynamic progress recognition andrecommendation computing platform 110, to establish rules or limitsassociated with the dynamic progress recognition and recommendationcomputing platform 110, to store and/or transmit data that may be usedto update and/or validate one or more machine learning datasets (e.g.,purchase data, user behavioral data, and the like) and the like.

The remote user computing devices 170, 175 may be used to communicatewith, for example, dynamic progress recognition and recommendationcomputing platform 110, receive and display notifications, transmitdata, such a global positioning system (GPS), sensor, or other data tothe dynamic progress recognition and recommendation computing platform110 for analysis and use in evaluating object data, identifyingalternatives, updating and/or validating machine learning datasets, andthe like.

In one or more arrangements, internal data computing device 120,external data computing device 130, local user computing device 150,local user computing device 155, remote user computing device 170,and/or remote user computing device 175 may be any type of computingdevice or combination of devices capable of performing the particularfunctions described herein. For example, internal data computing device120, external data computing device 130, local user computing device150, local user computing device 155, remote user computing device 170,and/or remote user computing device 175 may, in some instances, beand/or include server computers, desktop computers, laptop computers,tablet computers, smart phones, or the like that may include one or moreprocessors, memories, communication interfaces, storage devices, and/orother components. As noted above, and as illustrated in greater detailbelow, any and/or all of internal data computing device 120, externaldata computing device 130, local user computing device 150, local usercomputing device 155, remote user computing device 170, and/or remoteuser computing device 175 may, in some instances, be special-purposecomputing devices configured to perform specific functions.

Computing environment 100 also may include one or more computingplatforms. For example, and as noted above, computing environment 100may include dynamic progress recognition and recommendation computingplatform 110. As illustrated in greater detail below, dynamic progressrecognition and recommendation computing platform 110 may include one ormore computing devices configured to perform one or more of thefunctions described herein. For example, dynamic progress recognitionand recommendation computing platform 110 may include one or morecomputers (e.g., laptop computers, desktop computers, servers, serverblades, or the like).

As mentioned above, computing environment 100 also may include one ormore networks, which may interconnect one or more of dynamic progressrecognition and recommendation computing platform 110, internal datacomputing device 120, external data computing device 130, augmentedreality device 140, local user computing device 150, local usercomputing device 155, remote user computing device 170, and/or remoteuser computing device 175. For example, computing environment 100 mayinclude private network 190 and public network 195. Private network 190and/or public network 195 may include one or more sub-networks (e.g.,Local Area Networks (LANs), Wide Area Networks (WANs), or the like).Private network 190 may be associated with a particular organization(e.g., a corporation, financial institution, educational institution,governmental institution, or the like) and may interconnect one or morecomputing devices associated with the organization. For example, dynamicprogress recognition and recommendation computing platform 110, internaldata computing device 120, local user computing device 150, and localuser computing device 155, may be associated with an organization (e.g.,a financial institution), and private network 190 may be associated withand/or operated by the organization, and may include one or morenetworks (e.g., LANs, WANs, virtual private networks (VPNs), or thelike) that interconnect dynamic progress recognition and recommendationcomputing platform 110, internal data computing device 120, local usercomputing device 150, and local user computing device 155, and one ormore other computing devices and/or computer systems that are used by,operated by, and/or otherwise associated with the organization. Publicnetwork 195 may connect private network 190 and/or one or more computingdevices connected thereto (e.g., dynamic progress recognition andrecommendation computing platform 110, internal data computing device120, local user computing device 150, local user computing device 155)with one or more networks and/or computing devices that are notassociated with the organization. For example, external data computingdevice 130, augmented reality device 140, remote user computing device170, and/or remote user computing device 175 might not be associatedwith an organization that operates private network 190 (e.g., becauseexternal data computing device 130, augmented reality device 140, remoteuser computing device 170 and remote user computing device 175 may beowned, operated, and/or serviced by one or more entities different fromthe organization that operates private network 190, such as one or morecustomers of the organization, public or government entities, and/orvendors of the organization, rather than being owned and/or operated bythe organization itself or an employee or affiliate of theorganization), and public network 195 may include one or more networks(e.g., the internet) that connect external data computing device 130,augmented reality device 140, remote user computing device 170 andremote user computing device 175 to private network 190 and/or one ormore computing devices connected thereto (e.g., dynamic progressrecognition and recommendation computing platform 110, internal datacomputing device 120, local user computing device 150, local usercomputing device 155).

Referring to FIG. 1B, dynamic progress recognition and recommendationcomputing platform 110 may include one or more processors 111, memory112, and communication interface 113. A data bus may interconnectprocessor(s) 111, memory 112, and communication interface 113.Communication interface 113 may be a network interface configured tosupport communication between dynamic progress recognition andrecommendation computing platform 110 and one or more networks (e.g.,private network 190, public network 195, or the like). Memory 112 mayinclude one or more program modules having instructions that whenexecuted by processor(s) 111 cause dynamic progress recognition andrecommendation computing platform 110 to perform one or more functionsdescribed herein and/or one or more databases that may store and/orotherwise maintain information which may be used by such program modulesand/or processor(s) 111. In some instances, the one or more programmodules and/or databases may be stored by and/or maintained in differentmemory units of dynamic progress recognition and recommendationcomputing platform 110 and/or by different computing devices that mayform and/or otherwise make up dynamic progress recognition andrecommendation computing platform 110.

For example, memory 112 may have, store, and/or include an image/dataanalysis module 112 a. Image/data analysis module 112 a may storeinstructions and/or data that may cause or enable the dynamic progressrecognition and recommendation computing platform 110 to receive dataassociated with one or more objects (e.g., via an augmented realitydevice 140). For instance, the image/data analysis module 112 a mayreceive data from, for example, an image capture device of the augmentedreality device 140. The data may include images, video, machine-readablecodes, and the like. In some examples, the image/data analysis module112 a may analyze the received data (e.g., in real-time or nearreal-time) to identify one or more objects within the data. Forinstance, a user may capture an image of an item at a grocery store thatthe user is considering purchases and is being viewed through the lensesof the user's augmented reality device 140 (e.g., wearable, augmentedreality glasses). The image capture device of the augmented realitydevice 140 may capture an image of the object being viewed through thelenses and may transmit it to the image/data analysis module 112 a. Thedata may be analyzed to identify the object within the data (e.g., usingobject recognition, optical character recognition, machine-readable codescanning applications, or the like).

The dynamic progress recognition and recommendation computing platform110 may further have, store, and/or include a machine learning engine112 b and machine learning datasets 112 c. Machine learning engine 112 band machine learning datasets 112 c may store instructions and/or datathat cause or enable dynamic progress recognition and recommendationcomputing platform 110 to evaluate analyzed data (e.g., image data,identified object data, or the like) to determine characteristics of theidentified object, determine whether the identified object is in keepingwith pre-determined goals of the user, identify one or more alternativesor recommendations, and the like. The machine learning datasets 112 cmay be generated based on analyzed data (e.g., data from previouslyreceived data, data from internal data computing device 120, data fromexternal data computing device 130, and the like), raw data, and/orreceived from one or more outside sources.

The machine learning engine 112 b may receive data (e.g., data frominternal data computing device 120, external data computing device 130,data from augmented reality device 140, analyzed data from image/dataanalysis module 112 a, and the like) and, using one or more machinelearning algorithms, may generate one or more machine learning datasets112 c. Various machine learning algorithms may be used without departingfrom the invention, such as supervised learning algorithms, unsupervisedlearning algorithms, regression algorithms (e.g., linear regression,logistic regression, and the like), instance based algorithms (e.g.,learning vector quantization, locally weighted learning, and the like),regularization algorithms (e.g., ridge regression, least-angleregression, and the like), decision tree algorithms, Bayesianalgorithms, clustering algorithms, artificial neural network algorithms,and the like. Additional or alternative machine learning algorithms maybe used without departing from the invention. In some examples, themachine learning engine 112 b may analyze data to identify patterns ofactivity, sequences of activity, and the like, to generate one or moremachine learning datasets 112 c.

The machine learning datasets 112 c may include machine learning datalinking one or more objects to pre-stored costs, nutritional values,behaviors, rewards, or the like. For instance, one or more objects maybe linked to pre-stored characteristics of the object, such as cost, orthe like. In some examples, a location of a user may also be used (e.g.,particular retailer at which item is being purchased, or the like). Insome examples, location information may be obtained from a user device,such as augmented reality device 140, local user computing device 150,155, remote user computing device 170, 175, or the like.

In some examples, the machine learning datasets 112 c may includemachine learning data linking one or more objects, comparisons ofcharacteristics of an object to a pre-determined goal, and the like, toone or more alternatives or recommendations. Accordingly, if a userattempts to purchase an item that is outside of the budget of the user,the machine learning datasets 112 c may be used to identify arecommended alternative product.

The machine learning datasets 112 c may be updated and/or validatedbased on subsequent data received, for example, after an object isanalyzed/identified, after an alternative has been recommended, after anotification has been generated, after updating or validating data isreceived (e.g., after a purchase/non-purchase), or the like.

The machine learning datasets 112 c may be used by, for example, acharacteristic determination module 112 d. Characteristic determinationmodule 112 d may store instructions and/or data that may cause or enablethe dynamic progress recognition and recommendation computing platform110 to identify one or more characteristics of an identified object. Insome examples, the identified characteristics may include price or cost,nutritional value, overall value, ratings of a product, and the like.

The machine learning datasets 112 c, as well as characteristics of theobject determined by the characteristic determination module 112 d, mayalso be used by recommendation generation module 112 e. Recommendationgeneration module 112 e may store instructions and/or data that maycause or enable the dynamic progress recognition and recommendationcomputing platform 110 to compare the identified characteristics topre-stored goals, limits, or the like and/or to generate an indicationof progress toward a goal or limit and/or a recommendation. Forinstance, identified characteristics of the object, such as cost,nutritional value, and the like, may be compared to pre-stored budgetand/or nutritional goals to determine whether the object is in keepingwith the goals. If so, the recommendation generation module 112 e mayrecommend proceeding with purchase, consumption, or the like.Alternatively, if not, the recommendation generation module 112 e maygenerate a recommendation for an alternative object (e.g., a lower costversion, a healthier snack, or the like).

Dynamic progress recognition and recommendation computing platform 110may further have, store and/or include a notification generation module112 f. The notification generation module 112 f may have or includeinstructions and/or data that may cause or enable the dynamic progressrecognition and recommendation computing platform 110 to generate anotification and cause the notification to be displayed to a user. Forinstance, data or other notification may be generated (e.g., includingan indication of progress toward a goal, a recommended alternative, orthe like) and may be transmitted to a user device. For instance, thenotification may be transmitted to the augmented reality device 140 andmay be displayed on, for instance, the lenses of the augmented realitydevice 140. In some examples, the notification may overlay or bedisplayed simultaneously with the user viewing the object.

FIGS. 2A-2C depict an illustrative event sequence for implementing andusing dynamic progress recognition and recommendation functions inaccordance with one or more aspects described herein. The events shownin the illustrative event sequence are merely one example sequence andadditional events may be added, or events may be omitted, withoutdeparting from the invention.

Referring to FIG. 2A, at step 201, data may be transmitted from, forexample, internal data computing device 120. In some examples, the datamay include historical data, purchase or event processing data, and thelike. In step 202, data may be transmitted from external data computingdevice 130. In some examples, the data may include historicaltransaction or purchase data, nutritional information, recommendationinformation, and the like.

In step 203, the transmitted data may be received by the dynamicprogress recognition and recommendation computing platform 110 and maybe used to generate one or more machine learning datasets. In someexamples, additional or alternative data may also be used to generatethe machine learning datasets without departing from the invention.

In step 204, data may be captured by an augmented reality device 140. Insome examples, the augmented reality device 140 may be wearableaugmented reality glasses. In some arrangements, the data captured maybe image data (e.g., video, still or the like) of an object, bar code,or the like. In step 205, the captured data may be transmitted to thedynamic progress recognition and recommendation computing platform 110.

In step 206, the transmitted data may be received and analyzed (e.g., inreal-time or near real-time) to identify one or more objects within theimage data. For instance, object recognition, optical characterrecognition, machine-readable code scanning applications, or the like,may be used to identify one or more objects within the data received.

With reference to FIG. 2B, in step 207, the one or more objects (e.g.,objects viewed by the user through, for example, one or more clear orsemi-clear lenses within the augmented reality device 140) may beidentified. In step 208, one or more machine learning datasets may beused to identify one or more characteristics of the identified object.For instance, characteristics such as cost or price, nutritional value,overall value, ratings, and the like, may be determined or identified.

In step 209, the identified characteristics may be compared to one ormore pre-determined goals, limits, or the like, (e.g., a pre-determinedor pre-defined spending limit for a particular category of goods orservices, or the like) of the user. For instance, a cost of the objectmay be compared to a pre-determined budget of a user to determinewhether purchasing the object would be within the budget. In anotherexample, a user may pre-determine one or more nutritional goals orlimits. Accordingly, prior to purchasing an object, the system maycompare the nutritional value of the object to the pre-determinednutritional goals or limits to determine whether the object is inkeeping with the goals or limits.

In step 210, based on the comparison of the characteristics to thepre-determined goals or limits, one or more recommendations may begenerated. For instance, the recommendation may include an indicationthat the cost of the object is within budget or outside of budget. Inother examples, the recommendation may include a suggested alternativeor alternative object (e.g., a lower cost brand, an alternate itemhaving a greater nutritional value, or the like). In some examples, oneor more machine learning datasets may be used to generate therecommendation.

In step 211, a notification may be generated. The notification mayinclude the generated recommendation.

With reference to FIG. 2C, in step 212, the generated notification maybe transmitted to, for example, the augmented reality device 140. Insome examples, transmitting the notification may also includetransmitting a signal, command or instruction to display thenotification on the augmented reality device 140. In step 213, thenotification may be displayed on the augmented reality device 140. Forinstance, the signal, command or instruction may be executed by theaugmented reality device 140 to display the generated notification. Asdiscussed herein, displaying the notification may include overlaying ordisplaying the notification simultaneously with the object being viewedby the user through the lenses of the augmented reality device 140.

In step 214, data associated with whether the recommendation wasimplemented may be received. For instance, the image capture device ofthe augmented reality device 140 may capture an image of the objectbeing placed into a shopping cart, thereby indicating that the user haschosen to purchase the item. In another example, the augmented realitydevice 140 may receive user input (e.g., a tap or other selection) thatthe user is implementing the recommendation (e.g., to purchase theobject, to purchase a recommended alternative, or the like). In stillother examples, data may be received from other devices, such asinternal data computing device 120, external data computing device 130,local user computing device 150, 155, remote user computing device 170,175, or the like, indicating purchase data of the user and indicatingwhether the identified object was purchase/not purchases, whether anyrecommended alternative was purchased/not-purchased, and the like. Insome examples, this data may be received from an entity associated withan event processing device used to purchase the object.

In step 215, the received data may be transmitted to the dynamicprogress recognition and recommendation computing platform 110. In step216, the data may be used to update and/or validate one or more machinelearning datasets.

FIGS. 3A-3C illustrate one example arrangement for viewing objectsthough an augmented reality device to identify the object and receive anotification according to one or more aspects described herein. Withreference to FIG. 3A, an augmented reality device 300 is shown. Theaugmented reality device 300 is shown as a wearable eyeglass styleaugmented reality device. However, as discussed herein, other wearabledevice, smartphones, tablets, and other similar devices may be used asan augmented reality device without departing from the invention.

The augmented reality device 300 includes two lenses 302 through which auser may view objects. In some examples, the lenses may be clear orsemi-clear and may permit a user to view any objects withoutobstruction, in at least some arrangements. The augmented reality device300 may further include an image capture device 304. The image capturedevice 304 may be configured to capture images, such as video, stillimages and the like, of the object, a bar code or other machine readablecode associated with the object, text associated with the object, andthe like.

A user may wear the augmented reality device 300 and may view one ormore objects, such as object 310 through the lenses of the augmentedreality device 300. As shown in FIG. 3B, which provides a perspective ofthe object 310 as viewed through the lens 302 of the augmented realitydevice, the user may view the object. In some examples, as the user isviewing the object through the lens 302, the image capture device 304may captured data (e.g., images, video, or the like) of the object(e.g., via a lens associated with the image capture device and differentfrom the lens through which the user is viewing the object. The data maythen be transmitted from the augmented reality device 300 to, forexample, the dynamic progress recognition and recommendation computingplatform 110 for analysis, as discussed herein.

FIG. 3C again illustrates the object 310 as viewed through the lens 302of the augmented reality device and further includes notificationinformation 320. For instance, a notification generated, for example, bythe dynamic progress recognition and recommendation computing platform110 may be displayed on the lens 302 of the augmented reality device300. In some examples, and as shown in FIG. 3C, the notificationinformation may be displayed simultaneously with the user viewing theobject 310 through the lens 302.

FIGS. 4A-4C illustrate another example arrangement for viewing objectsthough an augmented reality device to identify the object and receive anotification according to one or more aspects described herein. Withreference to FIG. 4A, an augmented reality device 400 is shown. Theaugmented reality device 400 is shown as a wearable eyeglass styleaugmented reality device. However, as discussed herein, other wearabledevice, smartphones, tablets, and other similar devices may be used asan augmented reality device without departing from the invention.

The augmented reality device 400 includes two lenses 402 through which auser may view objects. In some examples, the lenses may be clear orsemi-clear and may permit a user to view any objects withoutobstruction, in at least some arrangements. The augmented reality device400 may further include an image capture device 404. The image capturedevice 404 may be configured to capture images, such as video, stillimages and the like, of the object, a bar code or other machine readablecode associated with the object, text associated with the object, andthe like.

A user may wear the augmented reality device 400 and may view one ormore objects, such as object 410 through the lenses of the augmentedreality device 400. As shown in FIG. 4B, which provides a perspective ofthe object 410 as viewed through the lens 402 of the augmented realitydevice, the user may view the object. In some examples, as the user isviewing the object 410 through the lens 402, the image capture device404 may captured data (e.g., images, video, or the like) of the object410 (e.g., via a lens associated with the image capture device anddifferent from the lens through which the user is viewing the object).For instance, the image capture device 404 may capture an image of thebar code 412 on the object 410. The data (e.g., image of the bar codeand/or bar code data) may then be transmitted from the augmented realitydevice 400 to, for example, the dynamic progress recognition andrecommendation computing platform 110 for analysis, as discussed herein.

FIG. 4C again illustrates the object 410 as viewed through the lens 402of the augmented reality device 400 and further includes notificationinformation 420. For instance, a notification generated, for example, bythe dynamic progress recognition and recommendation computing platform110 may be displayed on the lens 402 of the augmented reality device400. In some examples, and as shown in FIG. 4C, the notificationinformation may be displayed simultaneously with the user viewing theobject 410 through the lens 402.

FIG. 5 is a flow chart illustrating one example method of providingprogress recognition and recommendation functions according to one ormore aspects described herein. The processes illustrated in FIG. 5 aremerely some example processes and functions. The steps shown may beperformed in a different order, more steps may be added, or one or moresteps may be omitted without departing from the invention.

In step 500, data may be received. In some examples, data may bereceived from, for instance, an augmented reality device, such aswearable, augmented reality glasses. In some examples, the data receivedmay be image data including an image of an object being viewed by a userthrough a lens of the augmented reality device.

In step 502, the received data may be analyzed. In some examples,analyzing data may include using object recognition, optical characterrecognition, machine-readable code scanning, and the like, to identifyan object in the image data. In step 504, the object may be identified.

In step 506, one or more machine learning datasets may be used todetermine one or more characteristics of the object. For instance,machine learning may be used to determine a cost or price, a nutritionalvalue, or the like. In step 508, the determine characteristics may becompared to one or more pre-determined goals of the user. For instance,a cost of the object may be compared to a budget of the user.

In step 510, a determination may be made as to whether thecharacteristic is within or in keeping with the pre-determined goal. Ifnot, one or more machine learning datasets may be used to generate arecommendation or alternative in step 512. In step 514, a notificationmay be generated including the recommendation or alternative. In someexamples, information associated with the pre-determined goal may alsobe included in the notification (e.g., a balance remaining, a runningtally of amount spent toward a particular budget, or the like). In step516, the notification may be transmitted to the augmented reality deviceand displayed on the augmented reality device.

If, in step 510, the characteristics is within or in keeping with thepre-determined goal, a notification may be generated in step 518. Thenotification may include an indication that the item is within thepre-determined goal, may include a running balance or amount remainingtoward a goal or limit, or the like. In step 520, the notification maybe transmitted to the augmented reality device and may be displayed bythe augmented reality device.

As discussed herein, the arrangements described provide for use ofmachine learning datasets to identify characteristics of an identifiedobject, compare the characteristics to a pre-defined goal or limit,generate recommendations, and the like. As discussed herein, thesefunctions may be performed in real-time or near real-time to enableefficient, informed decisioning. The system also enables real-time ornear real-time feedback to be provided to a system in order to updateand/or validate one or more machine learning datasets based on whether arecommendation was implemented, whether an item was purchased, or thelike.

For instance, a user may be grocery shopping and may have a weeklygrocery shopping budget of $100. The user have an augmented realitydevice and may use the device to assist in staying within the budget byimplanting aspects described herein. For instance, a user may havewearable, augmented reality glassed through which he or she views eachitem selected for purchase at the grocery store. As an item is viewedthrough the lens of the augmented reality glasses (e.g., on the shelf,as the user picks it up, or the like), image data of the object may becaptured and transmitted to the progress recognition and recommendationcomputing platform 110 for analysis. The image data may be analyzed toidentify the object in the image, determine characteristics of theobject (e.g., price, nutritional value, or the like) and a determinationmay be made as to how purchasing the object may impact the user'sbudget. For example, the system may determine that purchasing the objectwould not exceed the weekly budget and may generate a notification fordisplay on the lens of the augmented reality device that purchasing theobject would not exceed the budget. The notification may also include arunning tally of an amount projected to be spent based on other objectsthe user has, for example, placed in a grocery cart, basket, or thelike. The running tally may aid the user in making smart financialdecisions.

In some examples, if purchase of an object would exceed the $100 grocerybudget, the system may generate a recommendation for an alternativepurchase. For example, another brand or a generic version of the objectmay have a lower cost. Accordingly, the system may recommend purchase ofthe other brand or generic version in order to stay within budget. Thisinformation may also be displayed on the lens of the augmented realitydevice. In some examples, directions to the identified alternative itemwithin the store may be generated and provided to the user (e.g., go toaisle 3, look down one shelf, or the like).

In addition, an indication may be provided to the progress recognitionand recommendation computing platform of items the user purchased,whether a recommendation was implemented, and the like. For instance, auser may select a button on the augmented reality device to indicatethat they are planning to move forward with a purchase. This data,coupled with image data of the object, may provide a more granularunderstanding of user purchasing, decisioning processes, user behaviors,and the like. This information may then be used to update and/orvalidate one or more machine learning datasets.

In some examples, the generated recommendations may include an incentiveto not make the potential purchase, purchase an alternative item, or thelike. For instance, the recommendation may include a rebate forpurchasing an alternative, a discount, refund, or the like.

While various aspects discussed herein are discussed in the context ofaugmented reality glasses, the functions described herein may beperformed using other augmented reality devices without departing fromthe invention. For instance, a user may capture an image of an objectwith, for example, a smartphone or other mobile device (e.g., using anapplication downloaded to the mobile device and executing thereon), afitness tracker or other wearable device, and the like. The image datamay then be analyzed and notifications may be transmitted to anddisplayed on the smartphone or other mobile device, fitness tracker orother wearable device, and the like.

Further, while various aspects and examples discussed herein arediscussed in the context of purchases in a grocery store, the functionsand aspects described herein may be used in various other contextswithout departing from the invention.

For instance, aspects described herein may be used when making purchasesat various retail establishments, at gas stations, and the like.Accordingly, the characteristics of the identified object may then becompared to pre-defined goals or budgets for that particular category ofgoods or services.

In some examples, aspects described herein may assist users in remainingon target regrading one or more nutritional goals. For instance, as auser selects food items for purchase, the nutritional value may bedetermined and notifications may be displayed to the user providing anindication of how this purchase may image the nutritional goals of theuser (e.g., is this a healthy snack, appropriate portion sizes,gluten-free, and the like).

In some examples, aspects described herein may be used to evaluate foodsa user is eating rather than purchasing. Accordingly, the system mayidentify foods being eaten in real-time and may generate notificationsindicating whether the food is a healthy food, whether it is in keepingwith nutritional goals, whether adjustments to other foods eatenthroughout the day should be made, and the like. Accordingly, the systemmay also aid in returning a user to a path that is on target with one ormore goals, even if the user has strayed from the path.

In some examples, the augmented reality device may be used tocommunicate with other devices in order to allocate a portion of a costto one or more other users, accounts, or the like. For instance, if apurchase is being made by two people, the augmented reality device maytransmit notifications to both parties and may generate an allocation ofa cost to each party. The allocated amount may then be compared topre-defined goals or limits, rather than an entire cost being allocatedto a single user and disproportionately impacting the budget of thesingle user.

In some arrangements, the system may include a user registrationprocess. The user registration process may include a user providingidentifying information, determining user specific goals, limits,budgets, or the like. In some examples, the user may customize one ormore aspects, such as how much over budget a purchase can be before anotification is transmitted, a threshold proximity to a budget at whicha notification is transmitted, and the like.

In some examples, if a user has an unexpected expense, the system maymodify one or more goals or budgets to account for the expense.Accordingly, the system may generate notifications reminding the user ofthe unexpected expense, adjusted budget, or the like, in order to aidthe user in recovering financial from the unexpected expense.

FIG. 6 depicts an illustrative operating environment in which variousaspects of the present disclosure may be implemented in accordance withone or more example embodiments. Referring to FIG. 6, computing systemenvironment 600 may be used according to one or more illustrativeembodiments. Computing system environment 600 is only one example of asuitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality contained in thedisclosure. Computing system environment 600 should not be interpretedas having any dependency or requirement relating to any one orcombination of components shown in illustrative computing systemenvironment 600.

Computing system environment 600 may include dynamic progressrecognition and recommendation computing device 601 having processor 603for controlling overall operation of dynamic progress recognition andrecommendation computing device 601 and its associated components,including Random Access Memory (RAM) 605, Read-Only Memory (ROM) 607,communications module 609, and memory 615. Dynamic progress recognitionand recommendation computing device 601 may include a variety ofcomputer readable media. Computer readable media may be any availablemedia that may be accessed by dynamic progress recognition andrecommendation computing device 601, may be non-transitory, and mayinclude volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, object code, data structures, programmodules, or other data. Examples of computer readable media may includeRandom Access Memory (RAM), Read Only Memory (ROM), ElectronicallyErasable Programmable Read-Only Memory (EEPROM), flash memory or othermemory technology, Compact Disk Read-Only Memory (CD-ROM), DigitalVersatile Disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired informationand that can be accessed by computing device 601.

Although not required, various aspects described herein may be embodiedas a method, a data transfer system, or as a computer-readable mediumstoring computer-executable instructions. For example, acomputer-readable medium storing instructions to cause a processor toperform steps of a method in accordance with aspects of the disclosedembodiments is contemplated. For example, aspects of method stepsdisclosed herein may be executed on a processor on dynamic progressrecognition and recommendation computing device 601. Such a processormay execute computer-executable instructions stored on acomputer-readable medium.

Software may be stored within memory 615 and/or storage to provideinstructions to processor 603 for enabling dynamic progress recognitionand recommendation computing device 601 to perform various functions asdiscussed herein. For example, memory 615 may store software used bydynamic progress recognition and recommendation computing device 601,such as operating system 617, application programs 619, and associateddatabase 621. Also, some or all of the computer executable instructionsfor dynamic progress recognition and recommendation computing device 601may be embodied in hardware or firmware. Although not shown, RAM 605 mayinclude one or more applications representing the application datastored in RAM 605 while dynamic progress recognition and recommendationcomputing device 601 is on and corresponding software applications(e.g., software tasks) are running on dynamic progress recognition andrecommendation computing device 601.

Communications module 609 may include a microphone, keypad, touchscreen, and/or stylus through which a user of dynamic progressrecognition and recommendation computing device 601 may provide input,and may also include one or more of a speaker for providing audio outputand a video display device for providing textual, audiovisual and/orgraphical output. Computing system environment 600 may also includeoptical scanners (not shown).

Dynamic progress recognition and recommendation computing device 601 mayoperate in a networked environment supporting connections to one or moreremote computing devices, such as computing devices 641 and 651.Computing devices 641 and 651 may be personal computing devices orservers that include any or all of the elements described above relativeto dynamic progress recognition and recommendation computing device 601.

The network connections depicted in FIG. 6 may include Local AreaNetwork (LAN) 625 and Wide Area Network (WAN) 629, as well as othernetworks. When used in a LAN networking environment, dynamic progressrecognition and recommendation computing device 601 may be connected toLAN 625 through a network interface or adapter in communications module609. When used in a WAN networking environment, dynamic progressrecognition and recommendation computing device 601 may include a modemin communications module 609 or other means for establishingcommunications over WAN 629, such as network 631 (e.g., public network,private network, Internet, intranet, and the like). The networkconnections shown are illustrative and other means of establishing acommunications link between the computing devices may be used. Variouswell-known protocols such as Transmission Control Protocol/InternetProtocol (TCP/IP), Ethernet, File Transfer Protocol (FTP), HypertextTransfer Protocol (HTTP) and the like may be used, and the system can beoperated in a client-server configuration to permit a user to retrieveweb pages from a web-based server. Any of various conventional webbrowsers can be used to display and manipulate data on web pages.

The disclosure is operational with numerous other computing systemenvironments or configurations. Examples of computing systems,environments, and/or configurations that may be suitable for use withthe disclosed embodiments include, but are not limited to, personalcomputers (PCs), server computers, hand-held or laptop devices, smartphones, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputers,mainframe computers, distributed computing environments that include anyof the above systems or devices, and the like and are configured toperform the functions described herein.

FIG. 7 depicts an illustrative block diagram of workstations and serversthat may be used to implement the processes and functions of certainaspects of the present disclosure in accordance with one or more exampleembodiments. Referring to FIG. 7, illustrative system 700 may be usedfor implementing example embodiments according to the presentdisclosure. As illustrated, system 700 may include one or moreworkstation computers 701. Workstation 701 may be, for example, adesktop computer, a smartphone, a wireless device, a tablet computer, alaptop computer, and the like, configured to perform various processesdescribed herein. Workstations 701 may be local or remote, and may beconnected by one of communications links 702 to computer network 703that is linked via communications link 705 to dynamic progressrecognition and recommendation server 704. In system 700, dynamicprogress recognition and recommendation server 704 may be a server,processor, computer, or data processing device, or combination of thesame, configured to perform the functions and/or processes describedherein. Server 704 may be used to receive data, analyze received data,identify an object, determine characteristics of the object, compare thedetermined characteristics to pre-determined goals or limits, generate arecommendation, generate a notification, and the like.

Computer network 703 may be any suitable computer network including theInternet, an intranet, a Wide-Area Network (WAN), a Local-Area Network(LAN), a wireless network, a Digital Subscriber Line (DSL) network, aframe relay network, an Asynchronous Transfer Mode network, a VirtualPrivate Network (VPN), or any combination of any of the same.Communications links 702 and 705 may be communications links suitablefor communicating between workstations 701 and dynamic progressrecognition and recommendation server 704, such as network links,dial-up links, wireless links, hard-wired links, as well as networktypes developed in the future, and the like.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,Application-Specific Integrated Circuits (ASICs), Field ProgrammableGate Arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,one or more steps described with respect to one figure may be used incombination with one or more steps described with respect to anotherfigure, and/or one or more depicted steps may be optional in accordancewith aspects of the disclosure.

What is claimed is:
 1. A system, comprising: an augmented reality deviceof a user; and a dynamic progress recognition and recommendationcomputing platform in communication with the augmented reality device,the dynamic progress recognition and recommendation computing platform,comprising: at least one processor; a communication interfacecommunicatively coupled to the at least one processor; and memorystoring computer-readable instructions that, when executed by the atleast one processor, cause the dynamic progress recognition andrecommendation computing platform to: receive, from the augmentedreality device of the user, image data; analyze, in real-time, the imagedata using at least one of: object recognition and optical characterrecognition to identify an object within the image; determine, using oneor more machine learning datasets, at least one characteristic of theidentified object; compare the determined at least one characteristic ofthe identified object to a pre-defined goal of the user; determine,based on the comparing, whether the at least one characteristic iswithin parameters of the pre-defined goal of the user; responsive todetermining that the at least one characteristic is within theparameters of the pre-defined goal of the user, generate a firstnotification including a progress toward the pre-defined goal of theuser; and transmit the generated first notification to the augmentedreality device of the user.
 2. The system of claim 1, further includinginstructions that, when executed, cause the dynamic progress recognitionand recommendation computing platform to: responsive to determining thatthe at least one characteristic is not within parameters of thepre-defined goal of the user, identify, based on the comparing and theone or more machine learning datasets, a recommended alternative objectto the identified object; and generate a second notification includingthe recommended alternative object.
 3. The system of claim 2, furtherincluding instructions that, when executed, cause the dynamic progressrecognition and recommendation computing platform to: receive, from theaugmented reality device of the user, data indicating whether therecommended alternative object was accepted; and updating the one ormore machine learning datasets based on the data indicating whether therecommended alternative object was accepted.
 4. The system of claim 1,further including instructions that, when executed, cause the dynamicprogress recognition and recommendation computing platform to: cause thegenerated first notification to be displayed on the augmented realitydevice of the user.
 5. The system of claim 1, wherein the image data isat least one of: video images and still images.
 6. The system of claim1, wherein the image data includes an image of a machine-readable codearranged on the object.
 7. The system of claim 1, wherein the augmentedreality device is a wearable device.
 8. The system of claim 7, whereinthe augmented reality device includes augmented reality glasses.
 9. Thesystem of claim 1, wherein the at least one characteristic is at leastone of: price and nutritional value.
 10. The system of claim 1, whereinthe pre-defined goal of the user is a budget associated with a categoryof goods.
 11. A method, comprising: at a computing platform comprisingat least one processor, memory, and a communication interface:receiving, by the at least one processor and from an augmented realitydevice of a user via the communication interface, image data; analyzing,in real-time and by the at least one processor, the image data using atleast one of: object recognition and optical character recognition toidentify an object within the image; determining, by the at least oneprocessor and using one or more machine learning datasets, at least onecharacteristic of the identified object; comparing, by the at least oneprocessor, the determined at least one characteristic of the identifiedobject to a pre-defined goal of the user; determining, based on thecomparing, whether the at least one characteristic is within parametersof the pre-defined goal of the user; if it is determined that the atleast one characteristic is within the parameters of the pre-definedgoal of the user, generating a first notification including a progresstoward the pre-defined goal of the user; and transmitting, by the atleast one processor, the generated first notification to the augmentedreality device of the user.
 12. The method of claim 11, furtherincluding: if it is determined that the at least one characteristic isnot within parameters of the pre-defined goal of the user, identifying,by the at least one processor and based on the comparing and the one ormore machine learning datasets, a recommended alternative object to theidentified object; and generating, by the at least one processor, asecond notification including the recommended alternative object. 13.The method of claim 11, further including: causing, by the at least oneprocessor, the generated first notification to be displayed on theaugmented reality device of the user.
 14. The method of claim 11,wherein the image data includes at least one of: video images and stillimages.
 15. The method of claim 11, wherein the image data includes animage of a machine-readable code arranged on the object.
 16. The methodof claim 11, wherein the augmented reality device includes augmentedreality glasses.
 17. The method of claim 11, wherein the at least onecharacteristic is at least one of: price and nutritional value.
 18. Themethod of claim 11, wherein the pre-defined goal of the user is a budgetassociated with a category of goods.
 19. One or more non-transitorycomputer-readable media storing instructions that, when executed by acomputing platform comprising at least one processor, memory, and acommunication interface, cause the computing platform to: receive, froman augmented reality device of a user, image data; analyze, inreal-time, the image data using at least one of: object recognition andoptical character recognition to identify an object within the image;determine, using one or more machine learning datasets, at least onecharacteristic of the identified object; compare the determined at leastone characteristic of the identified object to a pre-defined goal of theuser; determine, based on the comparing, whether the at least onecharacteristic is within parameters of the pre-defined goal of the user;responsive to determining that the at least one characteristic is withinthe parameters of the pre-defined goal of the user, generate a firstnotification including a progress toward the pre-defined goal of theuser; and transmit the generated first notification to the augmentedreality device of the user.
 20. The one or more non-transitorycomputer-readable media of claim 19, further including instructionsthat, when executed, cause the computing platform to: responsive todetermining that the at least one characteristic is not withinparameters of the pre-defined goal of the user, identify, based on thecomparing and the one or more machine learning datasets, a recommendedalternative object to the identified object; and generate a secondnotification including the recommended alternative object.
 21. The oneor more non-transitory computer-readable media of claim 19, furtherincluding instructions that, when executed, cause the computing platformto: cause the generated first notification to be displayed on theaugmented reality device of the user.
 22. The one or more non-transitorycomputer-readable media of claim 19, wherein the image data includes atleast one of: video images, still images and an image of amachine-readable code.
 23. The one or more non-transitorycomputer-readable media of claim 19, wherein the augmented realitydevice is a wearable device.
 24. The one or more non-transitorycomputer-readable media of claim 23, wherein the augmented realitydevice includes augmented reality glasses.
 25. The one or morenon-transitory computer-readable media of claim 19, wherein the at leastone characteristic is at least one of: price and nutritional value. 26.The one or more non-transitory computer-readable media of claim 19,wherein the pre-defined goal of the user is a budget associated with acategory of goods.